This disclosure relates generally to the field of imaging and more particularly to enhancing images obtained from Geiger-mode avalanche photodiode detectors using three-dimensional statistical differencing.
Imaging sensors, such as laser radar sensors (LADARs), acquire point clouds of a scene. The point cloud may be represented as a set of vertices (points) defined in particular coordinate system (e.g., X, Y, Z coordinates). The point clouds of the scene are then image processed to generate three-dimensional (3D) models of the actual environment present in the scene. The image processing used to create the 3D models can enhance the visualization and interpretation of the scene. Typical applications include surface measurements in airborne and ground-based industrial, commercial and military scanning applications such as site surveillance, terrain mapping, reconnaissance, bathymetry, autonomous control navigation and collision avoidance and the detection, ranging and recognition of remote military targets.
Presently there exist many types of LADARs for acquiring point clouds of a scene. A point cloud acquired by a LADAR typically comprise X, Y and Z coordinates from which range to target, two spatial angular measurements and strength (i.e., intensity) may be computed. However, the origins of many of the individual data points in the point cloud are indistinguishable from one another. As a result, most computations employed to generate the 3D models treat all of the points in the point cloud the same, thereby resulting in indistinguishable “humps/bumps” on the 3-D surface model of the scene.
In addition, various imaging processing techniques have been employed to reconstruct or otherwise clean up a blurred image of the scene. The blurring, or convolution, of the image is a result of the low resolution (i.e., the number of pixels/unit area) that may be obtained due to long distances between the image and the detector (e.g., low intensity) and distortion of the intensity image by the LADAR optics and by data processing. Accordingly, the image must be de-blurred (deconvolved).
In some cases LADARs include comprise arrays of avalanche photodiode (APD) detectors operating in Geiger-mode (hereinafter “GmAPD”) that are capable of detecting single photons incident onto one of the detectors. For example, FIG. 1 diagrammatically depicts a typical GmAPD LADAR system 10 that includes a focal plane array 12 of avalanche photodiode (APD) detectors 14 operating in Geiger-mode. Integrated timing and readout circuitry (not shown) is provided for each detector 14. In typical operation, a laser pulse emitted from a microchip laser 16 passes through a bandpass filter 18, variable divergence optics 20, a half-wave plate 22, a polarizing beam splitter 24, and is then directed via mirrors 26 and 28 through a beam expander 30 and a quarter wave plate 32. Scanning mirrors 34 then steer the laser pulses to scan the scene 36 of interest. It is noted that the scanning mirrors 34 may allow the imaging of large areas from a single angle of incidence or small areas imaged from a variety of angles on a single pass. Return reflections of the pulse from objects in the scene 36 (e.g., tree and tank) pass in the opposite direction through the polarizing beam splitter 24, a narrow band filter 38, and then through a zoom lens 40 onto the detector array 12. The outputs of the detector array 12 form a point cloud 42 of X, Y, Z data are then provided to an image processor 44 for viewing on a display 46.
More particularly, the operation of a GmAPD LADAR 10 may occur as follows. After the transmit laser pulse leaves the GmAPD LADAR 10, the detectors 14 are overbiased into Geiger-mode for a short time, corresponding to the expected time of arrival of the return pulse. The window in time when the GmAPD LADAR 10 is armed (e.g., the time the detectors 14 are overbiased) to receive the return pulse is known as the range gate. During the range gate the array 12 and the detectors 14 are sensitive to single photons. The high quantum efficiency in the array results in a high probability of generating a photoelectron. The few volts of overbias ensure that each free electron has a high probability of creating the growing avalanche which produces the volt-level pulse that is detected by the CMOS readout circuitry (not shown) of the array 12. This operation is more particularly described in U.S. Pat. No. 7,301,608, the disclosure of which is hereby incorporated by reference herein.
Unfortunately, during photon detection, the GmAPD system 10 does not distinguish among free electrons generated from laser pulses, background light, and thermal excitations within the absorber region (dark counts). High background and dark count rates are directly detrimental because they introduce noise (see FIG. 7 of U.S. Pat. No. 7,301,608) and are indirectly detrimental because they reduce the effective sensitivity to signal photons that arrive later in the range gate. See generally, M. Albota, “Three-dimensional imaging laser radar with a photon-counting avalanche photodiode array and microstrip laser”, Applied Optics, Vol. 41, No. 36, Dec. 20, 2002, the disclosure of which is hereby incorporated by reference herein. Nevertheless single photon counting GmAPD systems are favored due to efficient use of the power-aperture.
There presently exist several techniques for extracting the desired signal from the noise in a point cloud acquired by a GmAPD LADAR. Representative techniques include Z-Coincidence Processing (ZCP) that counts the number of points in fixed-size voxels to determine if a single return point is noise or a true return, Neighborhood Coincidence Processing (NCP) that considers points in neighboring voxels, and various hybrids thereof (NCP/ZCP). See P. Ramaswami, “Coincidence Processing of Geiger-Mode 3D Laser Radar Data”, Optical, Society of America, 2006, the disclosure of which is hereby incorporated by reference herein.
In addition to removal of noise from a point cloud through the use of NCP or ZCP techniques, it is often desirable to enhance the resulting image. Prior art image enhancement techniques include unsharp masking techniques using a highpass filter, techniques for emphasizing medium-contrast details more than large-contrast details using adaptive filters and statistical differential techniques that provide high enhancement in edges while presenting a low effect on homogenous areas.
As described in B. Remus, “Satellite Image Enhancement by Controlled Statistical Differentiation”, pp. 32-36, Innovations and Advanced Techniques in Systems Computing Sciences and Software Engineering, Springer Science+Business Media B.V. 2008, the disclosure of which is hereby incorporated by reference herein, statistical differention implies the division of original pixels F(j,k) by their standard deviation S(j,k):
                              G          ⁡                      (                          j              ,              k                        )                          =                              F            ⁡                          (                              j                ,                k                            )                                            S            ⁡                          (                              j                ,                k                            )                                                          (        1        )            where:
                              S          ⁡                      (                          j              ,              k                        )                          =                              1                          W              2                                ⁢                                    ∑                              m                =                                  j                  -                  w                                                            j                +                ω                                      ⁢                                          ∑                                  n                  =                                      k                    -                    w                                                                    k                  +                  w                                            ⁢                                                [                                                            F                      ⁡                                              (                                                  m                          ,                          n                                                )                                                              -                                          M                      ⁡                                              (                                                  j                          ,                          k                                                )                                                                              ]                                2                                                                        (        2        )            is the standard deviation computed for every pixel on a W×W window and W=2w+1. M(j,k) represents the estimated mean value for the pixel having coordinates (j,k) and computed on a same sized window:
                              M          ⁡                      (                          j              ,              k                        )                          =                              1                          W              2                                ⁢                                    ∑                              m                +                j                -                w                                            j                +                ω                                      ⁢                                          ∑                                  n                  =                                      k                    -                    w                                                                    k                  +                  w                                            ⁢                              F                ⁡                                  (                                      m                    ,                    n                                    )                                                                                        (        3        )            The enhanced image, G(j,k), has a significant increase in magnitude for pixels that are different from neighbors and a decrease of magnitude for similar pixels.
Another approach for enhancement includes:G(j,k)=M(j,k)+A(F(j,k)−M(j,k))  (4)with A, a constant influencing the degree of enhancement, having current values in the range of [0.2, 0.7].
As set described in R. H. Wallis, “An Approach for the Space Variant Restoration and Enhancement of Images”, Proceedings Symposium on Current Mathematical Problems in Image Science, Monterey, Calif., November, 1976, the disclosure of which is hereby incorporated by reference herein, the approach set forth by formula (4) above, may be extended to:
                              G          ⁡                      (                          j              ,              k                        )                          =                              M            d                    +                                                    s                d                                            s                ⁡                                  (                                      j                    ,                    k                                    )                                                      ⁢                          (                                                F                  ⁡                                      (                                          j                      ,                      k                                        )                                                  -                                  M                  ⁡                                      (                                          j                      ,                      k                                        )                                                              )                                                          (        5        )            employing a desired mean value, Md, and a desired standard deviation, Sd.
Wallis also describes a generalization of the differencing operation in which the enhanced image is forced to a specific form, which desired first-order and second-order moments:
                              G          ⁡                      (                          j              ,              k                        )                          =                                            [                                                F                  ⁡                                      (                                          j                      ,                      k                                        )                                                  -                                  M                  ⁡                                      (                                          j                      ,                      k                                        )                                                              ]                        ⁢                          ⌈                                                AS                  d                                                                      AS                    ⁡                                          (                                              j                        ,                        k                                            )                                                        -                                      S                    d                                                              ⌉                                +                      [                                          rM                d                            +                                                (                                      1                    -                    r                                    )                                ⁢                                  M                  ⁡                                      (                                          j                      ,                      k                                        )                                                                        ]                                              (        6        )            